6,310 research outputs found

    Mobile Robot Lab Project to Introduce Engineering Students to Fault Diagnosis in Mechatronic Systems

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    This document is a self-archiving copy of the accepted version of the paper. Please find the final published version in IEEEXplore: http://dx.doi.org/10.1109/TE.2014.2358551This paper proposes lab work for learning fault detection and diagnosis (FDD) in mechatronic systems. These skills are important for engineering education because FDD is a key capability of competitive processes and products. The intended outcome of the lab work is that students become aware of the importance of faulty conditions and learn to design FDD strategies for a real system. To this end, the paper proposes a lab project where students are requested to develop a discrete event dynamic system (DEDS) diagnosis to cope with two faulty conditions in an autonomous mobile robot task. A sample solution is discussed for LEGO Mindstorms NXT robots with LabVIEW. This innovative practice is relevant to higher education engineering courses related to mechatronics, robotics, or DEDS. Results are also given of the application of this strategy as part of a postgraduate course on fault-tolerant mechatronic systems.This work was supported in part by the Spanish CICYT under Project DPI2011-22443

    NASA space station automation: AI-based technology review

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    Research and Development projects in automation for the Space Station are discussed. Artificial Intelligence (AI) based automation technologies are planned to enhance crew safety through reduced need for EVA, increase crew productivity through the reduction of routine operations, increase space station autonomy, and augment space station capability through the use of teleoperation and robotics. AI technology will also be developed for the servicing of satellites at the Space Station, system monitoring and diagnosis, space manufacturing, and the assembly of large space structures

    Adoption of vehicular ad hoc networking protocols by networked robots

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    This paper focuses on the utilization of wireless networking in the robotics domain. Many researchers have already equipped their robots with wireless communication capabilities, stimulated by the observation that multi-robot systems tend to have several advantages over their single-robot counterparts. Typically, this integration of wireless communication is tackled in a quite pragmatic manner, only a few authors presented novel Robotic Ad Hoc Network (RANET) protocols that were designed specifically with robotic use cases in mind. This is in sharp contrast with the domain of vehicular ad hoc networks (VANET). This observation is the starting point of this paper. If the results of previous efforts focusing on VANET protocols could be reused in the RANET domain, this could lead to rapid progress in the field of networked robots. To investigate this possibility, this paper provides a thorough overview of the related work in the domain of robotic and vehicular ad hoc networks. Based on this information, an exhaustive list of requirements is defined for both types. It is concluded that the most significant difference lies in the fact that VANET protocols are oriented towards low throughput messaging, while RANET protocols have to support high throughput media streaming as well. Although not always with equal importance, all other defined requirements are valid for both protocols. This leads to the conclusion that cross-fertilization between them is an appealing approach for future RANET research. To support such developments, this paper concludes with the definition of an appropriate working plan

    The Use of Fault Trees for the Design of Robots for Hazardous Environments

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    This paper addresses the application of fault trees to the analysis of robot manipulator reliability and fault tolerance. Although a common and useful tool in other applications, fault trees have only recently been applied to robots. In addition, most of the fault tree analyses in robotics have focused on qualitative, rather than quantitative, analysis. Robotic manipulators present some special problems, due to the complex and strongly coupled nature of their subsystems, and also their wild response to subsystem failures. Additionally, there is a lack of reliability data for robots and their subsystems. There has traditionally been little emphasis on fault tolerance in the design of industrial robots, and data regarding operational robot failures is relatively scarce.National Science FoundationSandia National LaboratoryNAS

    병렬 및 λΆ„μ‚° μž„λ² λ””λ“œ μ‹œμŠ€ν…œμ„ μœ„ν•œ λͺ¨λΈ 기반 μ½”λ“œ 생성 ν”„λ ˆμž„μ›Œν¬

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    ν•™μœ„λ…Όλ¬Έ(박사)--μ„œμšΈλŒ€ν•™κ΅ λŒ€ν•™μ› :κ³΅κ³ΌλŒ€ν•™ 컴퓨터곡학뢀,2020. 2. ν•˜μˆœνšŒ.μ†Œν”„νŠΈμ›¨μ–΄ 섀계 생산성 및 μœ μ§€λ³΄μˆ˜μ„±μ„ ν–₯μƒμ‹œν‚€κΈ° μœ„ν•΄ λ‹€μ–‘ν•œ μ†Œν”„νŠΈμ›¨μ–΄ 개발 방법둠이 μ œμ•ˆλ˜μ—ˆμ§€λ§Œ, λŒ€λΆ€λΆ„μ˜ μ—°κ΅¬λŠ” μ‘μš© μ†Œν”„νŠΈμ›¨μ–΄λ₯Ό ν•˜λ‚˜μ˜ ν”„λ‘œμ„Έμ„œμ—μ„œ λ™μž‘μ‹œν‚€λŠ” 데에 μ΄ˆμ μ„ λ§žμΆ”κ³  μžˆλ‹€. λ˜ν•œ, μž„λ² λ””λ“œ μ‹œμŠ€ν…œμ„ κ°œλ°œν•˜λŠ” 데에 ν•„μš”ν•œ μ§€μ—°μ΄λ‚˜ μžμ› μš”κ΅¬ 사항에 λŒ€ν•œ λΉ„κΈ°λŠ₯적 μš”κ΅¬ 사항을 κ³ λ €ν•˜μ§€ μ•Šκ³  있기 λ•Œλ¬Έμ— 일반적인 μ†Œν”„νŠΈμ›¨μ–΄ 개발 방법둠을 μž„λ² λ””λ“œ μ†Œν”„νŠΈμ›¨μ–΄λ₯Ό κ°œλ°œν•˜λŠ” 데에 μ μš©ν•˜λŠ” 것은 μ ν•©ν•˜μ§€ μ•Šλ‹€. 이 λ…Όλ¬Έμ—μ„œλŠ” 병렬 및 λΆ„μ‚° μž„λ² λ””λ“œ μ‹œμŠ€ν…œμ„ λŒ€μƒμœΌλ‘œ ν•˜λŠ” μ†Œν”„νŠΈμ›¨μ–΄λ₯Ό λͺ¨λΈλ‘œ ν‘œν˜„ν•˜κ³ , 이λ₯Ό μ†Œν”„νŠΈμ›¨μ–΄ λΆ„μ„μ΄λ‚˜ κ°œλ°œμ— ν™œμš©ν•˜λŠ” 개발 방법둠을 μ†Œκ°œν•œλ‹€. 우리의 λͺ¨λΈμ—μ„œ μ‘μš© μ†Œν”„νŠΈμ›¨μ–΄λŠ” κ³„μΈ΅μ μœΌλ‘œ ν‘œν˜„ν•  수 μžˆλŠ” μ—¬λŸ¬ 개의 νƒœμŠ€ν¬λ‘œ 이루어져 있으며, ν•˜λ“œμ›¨μ–΄ ν”Œλž«νΌκ³Ό λ…λ¦½μ μœΌλ‘œ λͺ…μ„Έν•œλ‹€. νƒœμŠ€ν¬ κ°„μ˜ 톡신 및 λ™κΈ°ν™”λŠ” λͺ¨λΈμ΄ μ •μ˜ν•œ κ·œμ•½μ΄ μ •ν•΄μ Έ 있고, μ΄λŸ¬ν•œ κ·œμ•½μ„ 톡해 μ‹€μ œ ν”„λ‘œκ·Έλž¨μ„ μ‹€ν–‰ν•˜κΈ° 전에 μ†Œν”„νŠΈμ›¨μ–΄ μ—λŸ¬λ₯Ό 정적 뢄석을 톡해 확인할 수 있고, μ΄λŠ” μ‘μš©μ˜ 검증 λ³΅μž‘λ„λ₯Ό μ€„μ΄λŠ” 데에 κΈ°μ—¬ν•œλ‹€. μ§€μ •ν•œ ν•˜λ“œμ›¨μ–΄ ν”Œλž«νΌμ—μ„œ λ™μž‘ν•˜λŠ” ν”„λ‘œκ·Έλž¨μ€ νƒœμŠ€ν¬λ“€μ„ ν”„λ‘œμ„Έμ„œμ— λ§€ν•‘ν•œ 이후에 μžλ™μ μœΌλ‘œ ν•©μ„±ν•  수 μžˆλ‹€. μœ„μ˜ λͺ¨λΈ 기반 μ†Œν”„νŠΈμ›¨μ–΄ 개발 λ°©λ²•λ‘ μ—μ„œ μ‚¬μš©ν•˜λŠ” ν”„λ‘œκ·Έλž¨ ν•©μ„±κΈ°λ₯Ό λ³Έ λ…Όλ¬Έμ—μ„œ μ œμ•ˆν•˜μ˜€λŠ”λ°, λͺ…μ„Έν•œ ν”Œλž«νΌ μš”κ΅¬ 사항을 λ°”νƒ•μœΌλ‘œ 병렬 및 λΆ„μ‚° μž„λ² λ””λ“œ μ‹œμŠ€ν…œμ„μ—μ„œ λ™μž‘ν•˜λŠ” μ½”λ“œλ₯Ό μƒμ„±ν•œλ‹€. μ—¬λŸ¬ 개의 μ •ν˜•μ  λͺ¨λΈλ“€μ„ κ³„μΈ΅μ μœΌλ‘œ ν‘œν˜„ν•˜μ—¬ μ‘μš©μ˜ 동적 ν–‰νƒœλ₯Ό λ‚˜νƒ€κ³ , ν•©μ„±κΈ°λŠ” μ—¬λŸ¬ λͺ¨λΈλ‘œ κ΅¬μ„±λœ 계측적인 λͺ¨λΈλ‘œλΆ€ν„° 병렬성을 κ³ λ €ν•˜μ—¬ νƒœμŠ€ν¬λ₯Ό μ‹€ν–‰ν•  수 μžˆλ‹€. λ˜ν•œ, ν”„λ‘œκ·Έλž¨ ν•©μ„±κΈ°μ—μ„œ λ‹€μ–‘ν•œ ν”Œλž«νΌμ΄λ‚˜ λ„€νŠΈμ›Œν¬λ₯Ό 지원할 수 μžˆλ„λ‘ μ½”λ“œλ₯Ό κ΄€λ¦¬ν•˜λŠ” 방법도 보여주고 μžˆλ‹€. λ³Έ λ…Όλ¬Έμ—μ„œ μ œμ‹œν•˜λŠ” μ†Œν”„νŠΈμ›¨μ–΄ 개발 방법둠은 6개의 ν•˜λ“œμ›¨μ–΄ ν”Œλž«νΌκ³Ό 3 μ’…λ₯˜μ˜ λ„€νŠΈμ›Œν¬λ‘œ κ΅¬μ„±λ˜μ–΄ μžˆλŠ” μ‹€μ œ κ°μ‹œ μ†Œν”„νŠΈμ›¨μ–΄ μ‹œμŠ€ν…œ μ‘μš© μ˜ˆμ œμ™€ 이쒅 λ©€ν‹° ν”„λ‘œμ„Έμ„œλ₯Ό ν™œμš©ν•˜λŠ” 원격 λ”₯ λŸ¬λ‹ 예제λ₯Ό μˆ˜ν–‰ν•˜μ—¬ 개발 λ°©λ²•λ‘ μ˜ 적용 κ°€λŠ₯성을 μ‹œν—˜ν•˜μ˜€λ‹€. λ˜ν•œ, ν”„λ‘œκ·Έλž¨ ν•©μ„±κΈ°κ°€ μƒˆλ‘œμš΄ ν”Œλž«νΌμ΄λ‚˜ λ„€νŠΈμ›Œν¬λ₯Ό μ§€μ›ν•˜κΈ° μœ„ν•΄ ν•„μš”λ‘œ ν•˜λŠ” 개발 λΉ„μš©λ„ μ‹€μ œ μΈ‘μ • 및 μ˜ˆμΈ‘ν•˜μ—¬ μƒλŒ€μ μœΌλ‘œ 적은 λ…Έλ ₯으둜 μƒˆλ‘œμš΄ ν”Œλž«νΌμ„ 지원할 수 μžˆμŒμ„ ν™•μΈν•˜μ˜€λ‹€. λ§Žμ€ μž„λ² λ””λ“œ μ‹œμŠ€ν…œμ—μ„œ μ˜ˆμƒμΉ˜ λͺ»ν•œ ν•˜λ“œμ›¨μ–΄ μ—λŸ¬μ— λŒ€ν•΄ 결함을 κ°λ‚΄ν•˜λŠ” 것을 ν•„μš”λ‘œ ν•˜κΈ° λ•Œλ¬Έμ— 결함 감내에 λŒ€ν•œ μ½”λ“œλ₯Ό μžλ™μœΌλ‘œ μƒμ„±ν•˜λŠ” 연ꡬ도 μ§„ν–‰ν•˜μ˜€λ‹€. λ³Έ κΈ°λ²•μ—μ„œ 결함 감내 섀정에 따라 νƒœμŠ€ν¬ κ·Έλž˜ν”„λ₯Ό μˆ˜μ •ν•˜λŠ” 방식을 ν™œμš©ν•˜μ˜€μœΌλ©°, 결함 κ°λ‚΄μ˜ λΉ„κΈ°λŠ₯적 μš”κ΅¬ 사항을 μ‘μš© κ°œλ°œμžκ°€ μ‰½κ²Œ μ μš©ν•  수 μžˆλ„λ‘ ν•˜μ˜€λ‹€. λ˜ν•œ, 결함 감내 μ§€μ›ν•˜λŠ” 것과 κ΄€λ ¨ν•˜μ—¬ μ‹€μ œ μˆ˜λ™μœΌλ‘œ κ΅¬ν˜„ν–ˆμ„ κ²½μš°μ™€ λΉ„κ΅ν•˜μ˜€κ³ , 결함 μ£Όμž… 도ꡬλ₯Ό μ΄μš©ν•˜μ—¬ 결함 λ°œμƒ μ‹œλ‚˜λ¦¬μ˜€λ₯Ό μž¬ν˜„ν•˜κ±°λ‚˜, μž„μ˜λ‘œ 결함을 μ£Όμž…ν•˜λŠ” μ‹€ν—˜μ„ μˆ˜ν–‰ν•˜μ˜€λ‹€. λ§ˆμ§€λ§‰μœΌλ‘œ 결함 감내λ₯Ό μ‹€ν—˜ν•  λ•Œμ— ν™œμš©ν•œ 결함 μ£Όμž… λ„κ΅¬λŠ” λ³Έ λ…Όλ¬Έμ˜ 또 λ‹€λ₯Έ κΈ°μ—¬ 사항 쀑 ν•˜λ‚˜λ‘œ λ¦¬λˆ…μŠ€ ν™˜κ²½μœΌλ‘œ λŒ€μƒμœΌλ‘œ μ‘μš© μ˜μ—­ 및 컀널 μ˜μ—­μ— 결함을 μ£Όμž…ν•˜λŠ” 도ꡬλ₯Ό κ°œλ°œν•˜μ˜€λ‹€. μ‹œμŠ€ν…œμ˜ 견고성을 κ²€μ¦ν•˜κΈ° μœ„ν•΄ 결함을 μ£Όμž…ν•˜μ—¬ 결함 μ‹œλ‚˜λ¦¬μ˜€λ₯Ό μž¬ν˜„ν•˜λŠ” 것은 널리 μ‚¬μš©λ˜λŠ” λ°©λ²•μœΌλ‘œ, λ³Έ λ…Όλ¬Έμ—μ„œ 개발된 결함 μ£Όμž… λ„κ΅¬λŠ” μ‹œμŠ€ν…œμ΄ λ™μž‘ν•˜λŠ” 도쀑에 μž¬ν˜„ κ°€λŠ₯ν•œ 결함을 μ£Όμž…ν•  수 μžˆλŠ” 도ꡬ이닀. 컀널 μ˜μ—­μ—μ„œμ˜ 결함 μ£Όμž…μ„ μœ„ν•΄ 두 μ’…λ₯˜μ˜ 결함 μ£Όμž… 방법을 μ œκ³΅ν•˜λ©°, ν•˜λ‚˜λŠ” 컀널 GNU 디버거λ₯Ό μ΄μš©ν•œ 방법이고, λ‹€λ₯Έ ν•˜λ‚˜λŠ” ARM ν•˜λ“œμ›¨μ–΄ 브레이크포인트λ₯Ό ν™œμš©ν•œ 방법이닀. μ‘μš© μ˜μ—­μ—μ„œ 결함을 μ£Όμž…ν•˜κΈ° μœ„ν•΄ GDB 기반 결함 μ£Όμž… 방법을 μ΄μš©ν•˜μ—¬ 동일 μ‹œμŠ€ν…œ ν˜Ήμ€ 원격 μ‹œμŠ€ν…œμ˜ μ‘μš©μ— 결함을 μ£Όμž…ν•  수 μžˆλ‹€. 결함 μ£Όμž… 도ꡬ에 λŒ€ν•œ μ‹€ν—˜μ€ ODROID-XU4 λ³΄λ“œμ—μ„œ μ§„ν–‰ν•˜μ˜€λ‹€.While various software development methodologies have been proposed to increase the design productivity and maintainability of software, they usually focus on the development of application software running on a single processing element, without concern about the non-functional requirements of an embedded system such as latency and resource requirements. In this thesis, we present a model-based software development method for parallel and distributed embedded systems. An application is specified as a set of tasks that follow a set of given rules for communication and synchronization in a hierarchical fashion, independently of the hardware platform. Having such rules enables us to perform static analysis to check some software errors at compile time to reduce the verification difficulty. Platform-specific program is synthesized automatically after mapping of tasks onto processing elements is determined. The program synthesizer is also proposed to generate codes which satisfies platform requirements for parallel and distributed embedded systems. As multiple models which can express dynamic behaviors can be depicted hierarchically, the synthesizer supports to manage multiple task graphs with a different hierarchy to run tasks with parallelism. Also, the synthesizer shows methods of managing codes for heterogeneous platforms and generating various communication methods. The viability of the proposed software development method is verified with a real-life surveillance application that runs on six processing elements with three remote communication methods, and remote deep learning example is conducted to use heterogeneous multiprocessing components on distributed systems. Also, supporting a new platform and network requires a small effort by measuring and estimating development costs. Since tolerance to unexpected errors is a required feature of many embedded systems, we also support an automatic fault-tolerant code generation. Fault tolerance can be applied by modifying the task graph based on the selected fault tolerance configurations, so the non-functional requirement of fault tolerance can be easily adopted by an application developer. To compare the effort of supporting fault tolerance, manual implementation of fault tolerance is performed. Also, the fault tolerance method is tested with the fault injection tool to emulate fault scenarios and inject faults randomly. Our fault injection tool, which has used for testing our fault-tolerance method, is another work of this thesis. Emulating fault scenarios by intentionally injecting faults is commonly used to test and verify the robustness of a system. To emulate faults on an embedded system, we present a run-time fault injection framework that can inject a fault on both a kernel and application layer of Linux-based systems. For injecting faults on a kernel layer, two complementary fault injection techniques are used. One is based on Kernel GNU Debugger, and the other is using a hardware breakpoint supported by the ARM architecture. For application-level fault injection, the GDB-based fault injection method is used to inject a fault on a remote application. The viability of the proposed fault injection tool is proved by real-life experiments with an ODROID-XU4 system.Chapter 1 Introduction 1 1.1 Motivation 1 1.2 Contribution 6 1.3 Dissertation Organization 8 Chapter 2 Background 9 2.1 HOPES: Hope of Parallel Embedded Software 9 2.1.1 Software Development Procedure 9 2.1.2 Components of HOPES 12 2.2 Universal Execution Model 13 2.2.1 Task Graph Specification 13 2.2.2 Dataflow specification of an Application 15 2.2.3 Task Code Specification and Generic APIs 21 2.2.4 Meta-data Specification 23 Chapter 3 Program Synthesis for Parallel and Distributed Embedded Systems 24 3.1 Motivational Example 24 3.2 Program Synthesis Overview 26 3.3 Program Synthesis from Hierarchically-mixed Models 30 3.4 Platform Code Synthesis 33 3.5 Communication Code Synthesis 36 3.6 Experiments 40 3.6.1 Development Cost of Supporting New Platforms and Networks 40 3.6.2 Program Synthesis for the Surveillance System Example 44 3.6.3 Remote GPU-accelerated Deep Learning Example 46 3.7 Document Generation 48 3.8 Related Works 49 Chapter 4 Model Transformation for Fault-tolerant Code Synthesis 56 4.1 Fault-tolerant Code Synthesis Techniques 56 4.2 Applying Fault Tolerance Techniques in HOPES 61 4.3 Experiments 62 4.3.1 Development Cost of Applying Fault Tolerance 62 4.3.2 Fault Tolerance Experiments 62 4.4 Random Fault Injection Experiments 65 4.5 Related Works 68 Chapter 5 Fault Injection Framework for Linux-based Embedded Systems 70 5.1 Background 70 5.1.1 Fault Injection Techniques 70 5.1.2 Kernel GNU Debugger 71 5.1.3 ARM Hardware Breakpoint 72 5.2 Fault Injection Framework 74 5.2.1 Overview 74 5.2.2 Architecture 75 5.2.3 Fault Injection Techniques 79 5.2.4 Implementation 83 5.3 Experiments 90 5.3.1 Experiment Setup 90 5.3.2 Performance Comparison of Two Fault Injection Methods 90 5.3.3 Bit-flip Fault Experiments 92 5.3.4 eMMC Controller Fault Experiments 94 Chapter 6 Conclusion 97 Bibliography 99 μš” μ•½ 108Docto
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